This application relates generally to biometrics. More specifically, this application relates to methods and systems for performing biometric measurements that use spectral information.
“Biometrics” refers generally to the statistical analysis of characteristics of living bodies. One category of biometrics includes “biometric identification,” which commonly operates under one of two modes to provide automatic identification of people or to verify purported identities of people. Biometric sensing technologies measure the physical features or behavioral characteristics of a person and compare those features to similar prerecorded measurements to determine whether there is a match. Physical features that are commonly used for biometric identification include faces, irises, hand geometry, vein structure, and fingerprint patterns, which is the most prevalent of all biometric-identification features. Current methods for analyzing collected fingerprints include optical, capacitive, radio-frequency, thermal, ultrasonic, and several other less common techniques.
Most of the fingerprint-collection methods rely on measuring characteristics of the skin at or very near the surface of a finger. In particular, optical fingerprint readers typically rely on the presence or absence of a difference in the index of refraction between the sensor platen and the finger placed on it. When an air-filled valley of the fingerprint is above a particular location of the platen, total internal reflectance (“TIR”) occurs in the platen because of the air-platen index difference. Alternatively, if skin of the proper index of refraction is in optical contact with the platen, then the TIR at this location is “frustrated,” allowing light to traverse the platen-skin interface. A map of the differences in TIR across the region where the finger is touching the platen forms the basis for a conventional optical fingerprint reading. There are a number of optical arrangements used to detect this variation of the optical interface in both bright-field and dark-field optical arrangements. Commonly, a single, quasimonochromatic beam of light is used to perform this TIR-based measurement.
There also exists non-TIR optical fingerprint sensors. In most cases, these sensors rely on some arrangement of quasimonochromatic light to illuminate the front, sides, or back of a fingertip, causing the light to diffuse through the skin. The fingerprint image is formed due to the differences in light transmission across the skin-platen boundary for the ridge and valleys. The difference in optical transmission are due to changes in the Fresnel reflection characteristics due to the presence or absence of any intermediate air gap in the valleys, as known to one of familiarity in the art.
Optical fingerprint readers are particularly susceptible to image quality problems due to non-ideal conditions. If the skin is overly dry, the index match with the platen will be compromised, resulting in poor image contrast. Similarly, if the finger is very wet, the valleys may fill with water, causing an optical coupling to occur all across the fingerprint region and greatly reducing image contrast. Similar effects may occur if the pressure of the finger on the platen is too little or too great, the skin or sensor is dirty, the skin is aged and/or worn, or overly fine features are present such as may be the case for certain ethnic groups and in very young children. These effects decrease image quality and thereby decrease the overall performance of the fingerprint sensor. In some cases, commercial optical fingerprint readers incorporate a thin membrane of soft material such as silicone to help mitigate these effects and restore performance. As a soft material, the membrane is subject to damage, wear, and contamination, limiting the use of the sensor without maintenance.
Optical fingerprint readers, such as those based on TIR, as well as other modalities such as capacitance, RF, and others, typically produce images that are affected to some degree by the nonideal imaging conditions present during acquisition. An analysis of the textural characteristics of the resulting images is thus affected by the sampling conditions, which may limit or obscure the ability to observe the textural characteristics of the person's skin. The consequence of this is that texture is of limited utility in such sensing modalities.
Biometric sensors, particularly fingerprint biometric sensors, are generally prone to being defeated by various forms of spoof samples. In the case of fingerprint readers, a variety of methods are known in the art for presenting readers with a fingerprint pattern of an authorized user that is embedded in some kind of inanimate material such as paper, gelatin, epoxy, latex, and the like. Thus, even if a fingerprint reader can be considered to reliably determine the presence or absence of a matching fingerprint pattern, it is also critical to the overall system security to ensure that the matching pattern is being acquired from a genuine, living finger, which may be difficult to ascertain with many common sensors.
Another way in which some biometric systems may be defeated is through the use of a replay attack. In this scenario, an intruder records the signals coming from the sensor when an authorized user is using the system. At a later time, the intruder manipulates the sensor system such that the prerecorded authorized signals may be injected into the system, thereby bypassing the sensor itself and gaining access to the system secured by the biometric.
A common approach to making biometric sensors more robust, more secure, and less error-prone is to combine sources of biometric signals using an approach sometimes referred to in the art as using “dual,” “combinatoric,” “layered,” “fused,” “multibiometric,” or “multifactor biometric” sensing. To provide enhanced security in this way, biometric technologies are combined in such a way that different technologies measure portions of the body at the same time and are resistant to being defeated by using different samples or techniques to defeat the different sensors that are combined. When technologies are combined in a way that they view the same part of the body they are referred to as being “tightly coupled.”
The accuracy of noninvasive optical measurements of physiological analytes such as glucose, alcohol, hemoglobin, urea, and cholesterol can be adversely affected by variation of the skin tissue. In some cases it is advantageous to measure one or more physiological analytes in conjunction with a biometric measurement. Such dual measurement has potential interest and application to both commercial and law-enforcement markets.
There is accordingly a general need in the art for improved methods and systems for biometric sensing and analyte estimation using multispectral imaging systems and methods.